Introduction

The following produces predictions for health care needs in GA for COVID-19. The basis for this is a transmission simulation model, which is described here: http://2019-coronavirus-tracker.com/stochastic-GA.html

The transmission simulation model is run for different intervention scenarios. Output from this model is further processed here to produce estimates for health care needs in GA.

Assumptions

Below are assumptions with high and low values for different quantities that go into the model predictions.

Age structure for GA

We use the data below for population composition of GA. This age stratification is used below for risk calculations.

  • Total population: 10617423
  • Under 18y: 2526947
  • Between 18-60y: 7072541
  • Between 60-70y: 1017935
  • Above 70y: 784892

Hospitalization risk

We assume that among those that are cases (i.e. infected and tested positive), risk of hospitalization is as follows. This is based on (Wu and McGoogan 2020; Ferguson and al. 2020; Remuzzi and Remuzzi 2020). The first number is a low estimate, the second number a high end estimate. All values are percent.

  • Under 18y: 0.10%/1.00%
  • Between 18-60y: 1.00%/10.00%
  • Between 60-70y: 10.00%/25.00%
  • Above 70y: 20.00%/50.00%

Critical Care Risk

We assume that among those that are hospitalized, risk of critical care need is as follows. This is based on (Wu and McGoogan 2020; Ferguson and al. 2020; Remuzzi and Remuzzi 2020). The first number is a low estimate, the second number a high end estimate:

  • Under 18y: 1.00%/10.00%
  • Between 18-60y: 5.00%/15.00%
  • Between 60-70y: 20.00%/40.00%
  • Above 70y: 40.00%/70.00%

Risk of death

We assume that among cases, risk of death is as follows. This is based on (Verity et al. 2020). The first number is a low estimate, the second number a high end estimate:

  • Under 18y: 0.00%/0.01%
  • Between 18-60y: 0.10%/1.00%
  • Between 60-70y: 2.00%/8.00%
  • Above 70y: 5.00%/20.00%

Length of hospital stay

We assume that a hospital stay is between 10 (low) and 20 (high) days and independent of age. This is based on (Guan et al. 2020; Tindale et al. 2020; Sanche et al. 2020).

Model results

Figures and Tables with predictions for different infection spread scenarios. See here for more details on each scenario.

Results show predicted hospitalizations and critical care needs. For each variable, a low and high prediction is produced. Each variable is also reported as new individuals entering a given state (hospitalized/critical/etc.) at any given day and the total number of individuals in that state at any given date.

Result Figures

Result Tables

9. Social distancing with 555 initial infections and moderate presymptomatic transmission, step change in isolation, improving case ascertainment

Dates

Hosp_new_low

Hosp_new_high

Hosp_tot_low

Hosp_tot_high

Crit_new_low

Crit_new_high

Crit_tot_low

Crit_tot_high

Dead_new_low

Dead_new_high

Dead_tot_low

Dead_tot_high

2020-03-06

0

0

0

0

0

0

0

0

0

0

0

0

2020-03-07

0

0

0

0

0

0

0

0

0

0

0

0

2020-03-08

0

0

0

0

0

0

0

0

0

0

0

0

2020-03-09

0

0

0

0

0

0

0

0

0

0

0

0

2020-03-10

0

0

0

0

0

0

0

0

0

0

0

0

2020-03-11

0

0

0

0

0

0

0

0

0

0

0

0

2020-03-12

0

0

0

1

0

0

0

0

0

0

0

1

2020-03-13

0

0

0

1

0

0

0

0

0

0

0

1

2020-03-14

0

0

0

1

0

0

0

0

0

0

0

1

2020-03-15

0

0

0

1

0

0

0

1

0

0

0

1

2020-03-16

0

0

0

1

0

0

0

1

0

0

0

1

2020-03-17

0

0

0

1

0

0

0

1

0

0

0

1

2020-03-18

0

0

0

2

0

0

0

1

0

0

0

2

2020-03-19

0

1

1

3

0

1

0

1

0

1

1

3

2020-03-20

0

2

1

5

0

1

0

2

0

2

1

5

2020-03-21

1

4

2

9

0

2

1

4

1

4

2

9

2020-03-22

1

5

3

14

0

3

1

7

1

5

3

14

2020-03-23

1

7

4

20

0

3

1

10

1

7

4

20

2020-03-24

2

8

6

29

1

4

2

14

2

8

6

29

2020-03-25

2

9

8

38

1

4

2

19

2

9

8

38

2020-03-26

2

10

10

48

1

5

3

24

2

10

10

48

2020-03-27

2

11

12

59

1

5

4

29

2

11

13

59

2020-03-28

3

12

15

70

1

6

4

35

3

12

15

70

2020-03-29

3

12

17

82

1

6

5

41

3

12

18

83

2020-03-30

3

13

20

96

1

7

6

47

3

13

21

96

2020-03-31

3

14

23

109

1

7

7

54

3

14

24

110

2020-04-01

3

15

25

124

1

7

7

61

3

15

27

124

2020-04-02

3

15

27

139

1

7

8

69

3

15

30

139

2020-04-03

3

16

29

154

1

8

9

76

3

16

33

155

2020-04-04

4

16

31

171

1

8

9

84

4

16

37

172

2020-04-05

4

17

33

187

1

8

10

93

4

17

41

188

2020-04-06

4

17

34

205

1

9

10

101

4

17

44

206

2020-04-07

4

18

36

222

1

9

11

110

4

18

48

223

2020-04-08

4

18

37

240

1

9

11

119

4

18

52

242

2020-04-09

4

19

38

258

1

9

12

128

4

19

56

261

2020-04-10

4

19

40

275

1

9

12

136

4

19

60

280

2020-04-11

4

19

41

291

1

10

12

144

4

19

65

299

2020-04-12

4

20

42

305

1

10

13

151

4

20

69

319

2020-04-13

4

20

43

319

1

10

13

158

4

20

73

339

2020-04-14

4

21

44

331

1

10

13

164

4

21

78

360

2020-04-15

5

21

45

343

1

10

14

170

5

21

82

381

2020-04-16

5

21

46

355

1

11

14

175

5

21

87

402

2020-04-17

5

22

47

365

1

11

14

181

5

22

91

424

2020-04-18

5

22

48

375

1

11

15

186

5

22

96

446

2020-04-19

5

22

49

385

1

11

15

191

5

22

101

468

2020-04-20

5

23

50

395

2

11

15

195

5

23

106

491

2020-04-21

5

23

50

404

1

11

15

200

5

23

111

514

2020-04-22

5

23

51

413

2

12

16

204

5

23

116

537

2020-04-23

5

23

52

421

2

12

16

208

5

23

121

560

2020-04-24

5

24

53

429

2

12

16

212

5

24

126

584

2020-04-25

5

24

54

437

2

12

16

216

5

24

131

608

Comments

References

Ferguson, Neil, and et al. 2020. “Report 9: Impact of Non-Pharmaceutical Interventions (Npis) to Reduce Covid-19 Mortality and Healthcare Demand.” https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/news--wuhan-coronavirus/.

Guan, Wei-jie, Zheng-yi Ni, Yu Hu, Wen-hua Liang, Chun-quan Ou, Jian-xing He, Lei Liu, et al. 2020. “Clinical Characteristics of Coronavirus Disease 2019 in China.” New England Journal of Medicine 0 (0): null. https://doi.org/10.1056/NEJMoa2002032.

Li, Ruoran, Caitlin Rivers, Qi Tan, Megan B. Murray, Eric Toner, and Marc Lipsitch. 2020. “The Demand for Inpatient and ICU Beds for COVID-19 in the US: Lessons from Chinese Cities.” medRxiv, March, 2020.03.09.20033241. https://doi.org/10.1101/2020.03.09.20033241.

Remuzzi, Andrea, and Giuseppe Remuzzi. 2020. “COVID-19 and Italy: What Next?” The Lancet 0 (0). https://doi.org/10.1016/S0140-6736(20)30627-9.

Sanche, Steven, Yen Ting Lin, Chonggang Xu, Ethan Romero-Severson, Nick Hengartner, and Ruian Ke. 2020. “The Novel Coronavirus, 2019-nCoV, Is Highly Contagious and More Infectious Than Initially Estimated.” medRxiv, February, 2020.02.07.20021154. https://doi.org/10.1101/2020.02.07.20021154.

Tindale, Lauren, Michelle Coombe, Jessica E. Stockdale, Emma Garlock, Wing Yin Venus Lau, Manu Saraswat, Yen-Hsiang Brian Lee, et al. 2020. “Transmission Interval Estimates Suggest Pre-Symptomatic Spread of COVID-19.” medRxiv, March, 2020.03.03.20029983. https://doi.org/10.1101/2020.03.03.20029983.

Verity, Robert, Lucy C. Okell, Ilaria Dorigatti, Peter Winskill, Charles Whittaker, Natsuko Imai, Gina Cuomo-Dannenburg, et al. 2020. “Estimates of the Severity of COVID-19 Disease.” medRxiv, March, 2020.03.09.20033357. https://doi.org/10.1101/2020.03.09.20033357.

Wu, Zunyou, and Jennifer M. McGoogan. 2020. “Characteristics of and Important Lessons from the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases from the Chinese Center for Disease Control and Prevention.” JAMA, February. https://doi.org/10.1001/jama.2020.2648.